Abstract
We introduce Point2Skeleton, an unsupervised method to learn skeletal representations from point clouds. Existing skeletonization methods are limited to tubular shapes and the stringent requirement of watertight input, while our method aims to produce more generalized skeletal representations for complex structures and handle point clouds. Our key idea is to use the insights of the medial axis transform (MAT) to capture the intrinsic geometric and topological natures of the original input points. We first predict a set of skeletal points by learning a geometric transformation, and then analyze the connectivity of the skeletal points to form skeletal mesh structures. Extensive evaluations and comparisons show our method has superior performance and robustness. The learned skeletal representation will benefit several unsupervised tasks for point clouds, such as surface reconstruction and segmentation.
Original language | English |
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Title of host publication | Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 4275-4284 |
Number of pages | 10 |
ISBN (Electronic) | 978-1-6654-4509-2 |
ISBN (Print) | 978-1-6654-4510-8 |
DOIs | |
Publication status | Published - 2 Nov 2021 |
Event | IEEE Conference on Computer Vision and Pattern Recognition 2021 - Virtual Duration: 19 Jun 2021 → 25 Jun 2021 http://cvpr2021.thecvf.com/ |
Publication series
Name | Conference on Computer Vision and Pattern Recognition (CVPR) |
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Publisher | IEEE |
ISSN (Print) | 2575-7075 |
ISSN (Electronic) | 1063-6919 |
Conference
Conference | IEEE Conference on Computer Vision and Pattern Recognition 2021 |
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Abbreviated title | CVPR 2021 |
Period | 19/06/21 → 25/06/21 |
Internet address |